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       "     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
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   "source": [
    "from datapoint import DataPoint\n",
    "import numpy as np\n",
    "\n",
    "from bokeh.io import output_notebook, show\n",
    "from bokeh.plotting import figure\n",
    "from bokeh.models import HoverTool\n",
    "from bokeh.models import ColumnDataSource\n",
    "output_notebook()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "def parse_in_data(file_path):\n",
    "  \"\"\"\n",
    "    Args:\n",
    "    file_path \n",
    "\n",
    "          R 8.46642 0.0287602 -3.04035  1477010443399637  8.6 0.25  -3.00029  0\n",
    "          (R) (rho) (phi) (drho) (timestamp) (real x) (real y) (real vx) (real vy)\n",
    "\n",
    "          L 8.44818 0.251553  1477010443449633  8.45  0.25  -3.00027  0\n",
    "          (R) (x) (y) (timestamp) (real x) (real y) (real vx) (real vy)\n",
    "\n",
    "    Returns:\n",
    "      all_sensor_data, all_ground_truths\n",
    "      - two lists of DataPoint() instances \n",
    "\n",
    "  \"\"\"\n",
    "\n",
    "  all_sensor_data = []\n",
    "  all_ground_truths = []  \n",
    "\n",
    "  with open(file_path) as f:\n",
    "      \n",
    "    for line in f:\n",
    "      data = line.split() \n",
    "      \n",
    "      if data[0]  == 'L':\n",
    "        \n",
    "        sensor_data = DataPoint({ \n",
    "          'timestamp': int(data[3]),\n",
    "          'name': 'lidar',\n",
    "          'x': float(data[1]), \n",
    "          'y': float(data[2])\n",
    "        })\n",
    "        \n",
    "        g = {'timestamp': int(data[3]),\n",
    "             'name': 'state',\n",
    "             'x': float(data[4]),\n",
    "             'y': float(data[5]),\n",
    "             'vx': float(data[6]),\n",
    "             'vy': float(data[7])\n",
    "        }\n",
    "          \n",
    "        ground_truth = DataPoint(g)\n",
    "                \n",
    "      elif data[0] == 'R':\n",
    "          \n",
    "        sensor_data = DataPoint({ \n",
    "          'timestamp': int(data[4]),\n",
    "          'name': 'radar',\n",
    "          'rho': float(data[1]), \n",
    "          'phi': float(data[2]),\n",
    "          'drho': float(data[3])\n",
    "        })\n",
    "       \n",
    "        g = {'timestamp': int(data[4]),\n",
    "             'name': 'state',\n",
    "             'x': float(data[5]),\n",
    "             'y': float(data[6]),\n",
    "             'vx': float(data[7]),\n",
    "             'vy': float(data[8])\n",
    "        }\n",
    "        ground_truth = DataPoint(g)  \n",
    "\n",
    "      all_sensor_data.append(sensor_data)\n",
    "      all_ground_truths.append(ground_truth)\n",
    "\n",
    "  return all_sensor_data, all_ground_truths"
   ]
  },
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   "execution_count": 3,
   "metadata": {
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   "source": [
    "def parse_prediction_data(file_path):\n",
    "  \n",
    "  all_predictions = []\n",
    "    \n",
    "  with open(file_path) as f:\n",
    "    \n",
    "    for line in f:\n",
    "      data = line.split()\n",
    "      \n",
    "      p = {'timestamp': None,\n",
    "           'name': 'state',\n",
    "           'x': float(data[0]),\n",
    "           'y': float(data[1]),\n",
    "           'vx': float(data[2]),\n",
    "           'vy': float(data[3])\n",
    "        }\n",
    "    \n",
    "      all_predictions.append(DataPoint(p))\n",
    "        \n",
    "  return all_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "all_sensor_data, all_ground_truths = parse_in_data(\"data/data-1.txt\")\n",
    "all_state_estimations = parse_prediction_data(\"data/out-1-D.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "lidar_xs, lidar_ys = [], []\n",
    "radar_xs, radar_ys, radar_angles = [], [], []\n",
    "truth_xs, truth_ys, truth_angles = [], [], []\n",
    "state_xs, state_ys, state_angles = [], [], []\n",
    "    \n",
    "for s, t, p in zip(all_sensor_data, all_ground_truths, all_state_estimations):\n",
    "    \n",
    "  if s.get_name() == \"lidar\":\n",
    "    x, y = s.get_raw()\n",
    "    lidar_xs.append(x)\n",
    "    lidar_ys.append(y)\n",
    "  else:\n",
    "    x, y, vx, vy = s.get()\n",
    "    angle = np.arctan2(vy, vx)\n",
    "    radar_xs.append(x)\n",
    "    radar_ys.append(y)\n",
    "    radar_angles.append(angle)\n",
    "\n",
    "  x, y, vx, vy = t.get()\n",
    "  t_angle =  np.arctan2(vy, vy)\n",
    "  truth_xs.append(x)\n",
    "  truth_ys.append(y)\n",
    "  truth_angles.append(t_angle)\n",
    "\n",
    "  x, y, vx, vy = p.get()\n",
    "  p_angle =  np.arctan2(vy, vx)\n",
    "  state_xs.append(x)\n",
    "  state_ys.append(y)\n",
    "  state_angles.append(p_angle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
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   "outputs": [],
   "source": [
    "radar_source = ColumnDataSource(data = {\n",
    "    'x' : radar_xs,\n",
    "    'y' : radar_ys,\n",
    "    'angle': radar_angles, \n",
    "})\n",
    "\n",
    "truth_source = ColumnDataSource(data = {\n",
    "    'x' : truth_xs,\n",
    "    'y' : truth_ys,\n",
    "    'angle': truth_angles, \n",
    "})\n",
    "\n",
    "state_source = ColumnDataSource(data = {\n",
    "    'x' : state_xs,\n",
    "    'y' : state_ys,\n",
    "    'angle': truth_angles, \n",
    "})\n",
    "\n",
    "lidar_source = ColumnDataSource(data = {\n",
    "    'x' : lidar_xs,\n",
    "    'y' : lidar_ys,\n",
    "})"
   ]
  },
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   "execution_count": 7,
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       "            var render_items = [{\"docid\":\"1ed07761-f237-49e4-b1ea-6ffecc2153b7\",\"elementid\":\"bcd3461f-e60f-4437-a213-b1395efda744\",\"modelid\":\"1180fe3e-5b9a-4cea-861c-936dc1837408\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
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       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"bcd3461f-e60f-4437-a213-b1395efda744\")).parents('.cell').data().cell;\n",
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       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hover1 = HoverTool( tooltips = [\n",
    "            (\"index\", \"$index\"),\n",
    "            (\"x , y\", \"$x, $y\"),\n",
    "            (\"angle radians\", \"@angle\")])\n",
    "\n",
    "p = figure(plot_width = 1000, plot_height = 700, tools = [hover1])\n",
    "\n",
    "p.square(\n",
    "  'x', 'y', size = 5, \n",
    "  fill_color = \"orange\",\n",
    "  line_color = \"orange\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"radar measurements\", \n",
    "  source = radar_source)\n",
    "\n",
    "p.circle(\n",
    "  'x', 'y', size = 15, \n",
    "  fill_color = \"green\",\n",
    "  line_color = \"white\",\n",
    "  fill_alpha = 0.2,\n",
    "  line_width = 1, \n",
    "  legend = \"lidar measurements\", \n",
    "  source = lidar_source)\n",
    "\n",
    "p.triangle(\n",
    "  'x', 'y', size = 7, \n",
    "  fill_color = \"violet\",\n",
    "  line_color = \"violet\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 0.5, \n",
    "  legend = \"ground truth\",\n",
    "  source = truth_source)\n",
    "\n",
    "\n",
    "p.triangle(\n",
    "  'x', 'y', size = 2, \n",
    "  fill_color = \"black\",\n",
    "  line_color = \"black\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"state predictions\", \n",
    "  source = state_source)\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
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       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
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       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
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       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
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       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
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       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
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       "      for (var i = 0; i < js_urls.length; i++) {\n",
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       "          console.warn(\"failed to load library \" + url);\n",
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       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
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       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid '35d25845-a8d5-4b7e-a06b-c2a325a31732' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
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       "</script>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hover2 = HoverTool( tooltips = [\n",
    "            (\"index\", \"$index\"),\n",
    "            (\"x , y\", \"$x, $y\"),\n",
    "            (\"angle radians\", \"@angle\")])\n",
    "\n",
    "p = figure(plot_width = 1000, plot_height = 700, tools = [hover2])\n",
    "\n",
    "p.square(\n",
    "  'x', 'y', size = 5, \n",
    "  fill_color = \"violet\",\n",
    "  line_color = \"violet\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"radar measurements\", \n",
    "  source = radar_source)\n",
    "\n",
    "p.circle(\n",
    "  'x', 'y', size = 10, \n",
    "  fill_color = \"green\",\n",
    "  line_color = \"white\",\n",
    "  fill_alpha = 0.4,\n",
    "  line_width = 1, \n",
    "  legend = \"lidar measurements\", \n",
    "  source = lidar_source)\n",
    "\n",
    "p.line(state_xs, state_ys, line_width = 2, color='orange', legend = \"state predictions\")\n",
    "p.line(truth_xs, truth_ys, line_dash = \"4 4\", line_width = 1, color='navy', legend = \"ground truth\")\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "all_sensor_data, all_ground_truths = parse_in_data(\"data/data-2.txt\")\n",
    "all_state_estimations = parse_prediction_data(\"data/out-2-D.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "lidar_xs, lidar_ys = [], []\n",
    "radar_xs, radar_ys, radar_angles = [], [], []\n",
    "truth_xs, truth_ys, truth_angles = [], [], []\n",
    "state_xs, state_ys, state_angles = [], [], []\n",
    "    \n",
    "for s, t, p in zip(all_sensor_data, all_ground_truths, all_state_estimations):\n",
    "    \n",
    "  if s.get_name() == \"lidar\":\n",
    "    x, y = s.get_raw()\n",
    "    lidar_xs.append(x)\n",
    "    lidar_ys.append(y)\n",
    "  else:\n",
    "    x, y, vx, vy = s.get()\n",
    "    angle = np.arctan2(vy, vx)\n",
    "    radar_xs.append(x)\n",
    "    radar_ys.append(y)\n",
    "    radar_angles.append(angle)\n",
    "\n",
    "  x, y, vx, vy = t.get()\n",
    "  t_angle =  np.arctan2(vy, vy)\n",
    "  truth_xs.append(x)\n",
    "  truth_ys.append(y)\n",
    "  truth_angles.append(t_angle)\n",
    "\n",
    "  x, y, vx, vy = p.get()\n",
    "  p_angle =  np.arctan2(vy, vx)\n",
    "  state_xs.append(x)\n",
    "  state_ys.append(y)\n",
    "  state_angles.append(p_angle)\n",
    "\n",
    "radar_source = ColumnDataSource(data = {\n",
    "    'x' : radar_xs,\n",
    "    'y' : radar_ys,\n",
    "    'angle': radar_angles, \n",
    "})\n",
    "\n",
    "truth_source = ColumnDataSource(data = {\n",
    "    'x' : truth_xs,\n",
    "    'y' : truth_ys,\n",
    "    'angle': truth_angles, \n",
    "})\n",
    "\n",
    "state_source = ColumnDataSource(data = {\n",
    "    'x' : state_xs,\n",
    "    'y' : state_ys,\n",
    "    'angle': truth_angles, \n",
    "})\n",
    "\n",
    "lidar_source = ColumnDataSource(data = {\n",
    "    'x' : lidar_xs,\n",
    "    'y' : lidar_ys,\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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Application\",\"version\":\"0.12.4\"}};\n",
       "            var render_items = [{\"docid\":\"317dc1b0-2a1e-4520-b44b-bb38bfa2df7c\",\"elementid\":\"edb7e660-91ad-40e2-be58-7955d20c39d6\",\"modelid\":\"8bdf5c4d-4d3d-4801-8802-0a6342dc8b4c\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"edb7e660-91ad-40e2-be58-7955d20c39d6\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hover3 = HoverTool( tooltips = [\n",
    "            (\"index\", \"$index\"),\n",
    "            (\"x , y\", \"$x, $y\"),\n",
    "            (\"angle radians\", \"@angle\")])\n",
    "\n",
    "p = figure(plot_width = 1000, plot_height = 700, tools = [hover3])\n",
    "\n",
    "p.square(\n",
    "  'x', 'y', size = 5, \n",
    "  fill_color = \"orange\",\n",
    "  line_color = \"orange\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"radar measurements\", \n",
    "  source = radar_source)\n",
    "\n",
    "p.circle(\n",
    "  'x', 'y', size = 15, \n",
    "  fill_color = \"green\",\n",
    "  line_color = \"white\",\n",
    "  fill_alpha = 0.2,\n",
    "  line_width = 1, \n",
    "  legend = \"lidar measurements\", \n",
    "  source = lidar_source)\n",
    "\n",
    "p.triangle(\n",
    "  'x', 'y', size = 7, \n",
    "  fill_color = \"violet\",\n",
    "  line_color = \"violet\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 0.5, \n",
    "  legend = \"ground truth\",\n",
    "  source = truth_source)\n",
    "\n",
    "\n",
    "p.triangle(\n",
    "  'x', 'y', size = 2, \n",
    "  fill_color = \"black\",\n",
    "  line_color = \"black\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"state predictions\", \n",
    "  source = state_source)\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "show(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "\n",
       "    <div class=\"bk-root\">\n",
       "        <div class=\"bk-plotdiv\" id=\"dda9ca82-0448-463b-9b75-d29dd4f00ec7\"></div>\n",
       "    </div>\n",
       "<script type=\"text/javascript\">\n",
       "  \n",
       "  (function(global) {\n",
       "    function now() {\n",
       "      return new Date();\n",
       "    }\n",
       "  \n",
       "    var force = false;\n",
       "  \n",
       "    if (typeof (window._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
       "      window._bokeh_onload_callbacks = [];\n",
       "      window._bokeh_is_loading = undefined;\n",
       "    }\n",
       "  \n",
       "  \n",
       "    \n",
       "    if (typeof (window._bokeh_timeout) === \"undefined\" || force === true) {\n",
       "      window._bokeh_timeout = Date.now() + 0;\n",
       "      window._bokeh_failed_load = false;\n",
       "    }\n",
       "  \n",
       "    var NB_LOAD_WARNING = {'data': {'text/html':\n",
       "       \"<div style='background-color: #fdd'>\\n\"+\n",
       "       \"<p>\\n\"+\n",
       "       \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
       "       \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
       "       \"</p>\\n\"+\n",
       "       \"<ul>\\n\"+\n",
       "       \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
       "       \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
       "       \"</ul>\\n\"+\n",
       "       \"<code>\\n\"+\n",
       "       \"from bokeh.resources import INLINE\\n\"+\n",
       "       \"output_notebook(resources=INLINE)\\n\"+\n",
       "       \"</code>\\n\"+\n",
       "       \"</div>\"}};\n",
       "  \n",
       "    function display_loaded() {\n",
       "      if (window.Bokeh !== undefined) {\n",
       "        document.getElementById(\"dda9ca82-0448-463b-9b75-d29dd4f00ec7\").textContent = \"BokehJS successfully loaded.\";\n",
       "      } else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(display_loaded, 100)\n",
       "      }\n",
       "    }\n",
       "  \n",
       "    function run_callbacks() {\n",
       "      window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
       "      delete window._bokeh_onload_callbacks\n",
       "      console.info(\"Bokeh: all callbacks have finished\");\n",
       "    }\n",
       "  \n",
       "    function load_libs(js_urls, callback) {\n",
       "      window._bokeh_onload_callbacks.push(callback);\n",
       "      if (window._bokeh_is_loading > 0) {\n",
       "        console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
       "        return null;\n",
       "      }\n",
       "      if (js_urls == null || js_urls.length === 0) {\n",
       "        run_callbacks();\n",
       "        return null;\n",
       "      }\n",
       "      console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
       "      window._bokeh_is_loading = js_urls.length;\n",
       "      for (var i = 0; i < js_urls.length; i++) {\n",
       "        var url = js_urls[i];\n",
       "        var s = document.createElement('script');\n",
       "        s.src = url;\n",
       "        s.async = false;\n",
       "        s.onreadystatechange = s.onload = function() {\n",
       "          window._bokeh_is_loading--;\n",
       "          if (window._bokeh_is_loading === 0) {\n",
       "            console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
       "            run_callbacks()\n",
       "          }\n",
       "        };\n",
       "        s.onerror = function() {\n",
       "          console.warn(\"failed to load library \" + url);\n",
       "        };\n",
       "        console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
       "        document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "      }\n",
       "    };var element = document.getElementById(\"dda9ca82-0448-463b-9b75-d29dd4f00ec7\");\n",
       "    if (element == null) {\n",
       "      console.log(\"Bokeh: ERROR: autoload.js configured with elementid 'dda9ca82-0448-463b-9b75-d29dd4f00ec7' but no matching script tag was found. \")\n",
       "      return false;\n",
       "    }\n",
       "  \n",
       "    var js_urls = [];\n",
       "  \n",
       "    var inline_js = [\n",
       "      function(Bokeh) {\n",
       "        (function() {\n",
       "          var fn = function() {\n",
       "            var docs_json = 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       "            var render_items = [{\"docid\":\"69adb8d2-0555-47bf-b240-1f87167e8e61\",\"elementid\":\"dda9ca82-0448-463b-9b75-d29dd4f00ec7\",\"modelid\":\"5230001f-eaa3-48eb-b87c-efc5791bd271\"}];\n",
       "            \n",
       "            Bokeh.embed.embed_items(docs_json, render_items);\n",
       "          };\n",
       "          if (document.readyState != \"loading\") fn();\n",
       "          else document.addEventListener(\"DOMContentLoaded\", fn);\n",
       "        })();\n",
       "      },\n",
       "      function(Bokeh) {\n",
       "      }\n",
       "    ];\n",
       "  \n",
       "    function run_inline_js() {\n",
       "      \n",
       "      if ((window.Bokeh !== undefined) || (force === true)) {\n",
       "        for (var i = 0; i < inline_js.length; i++) {\n",
       "          inline_js[i](window.Bokeh);\n",
       "        }if (force === true) {\n",
       "          display_loaded();\n",
       "        }} else if (Date.now() < window._bokeh_timeout) {\n",
       "        setTimeout(run_inline_js, 100);\n",
       "      } else if (!window._bokeh_failed_load) {\n",
       "        console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
       "        window._bokeh_failed_load = true;\n",
       "      } else if (force !== true) {\n",
       "        var cell = $(document.getElementById(\"dda9ca82-0448-463b-9b75-d29dd4f00ec7\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
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   "source": [
    "hover4 = HoverTool( tooltips = [\n",
    "            (\"index\", \"$index\"),\n",
    "            (\"x , y\", \"$x, $y\"),\n",
    "            (\"angle radians\", \"@angle\")])\n",
    "\n",
    "p = figure(plot_width = 1000, plot_height = 700, tools = [hover4])\n",
    "\n",
    "p.square(\n",
    "  'x', 'y', size = 5, \n",
    "  fill_color = \"violet\",\n",
    "  line_color = \"violet\",\n",
    "  fill_alpha = 1,\n",
    "  angle = 'angle', \n",
    "  line_width = 1, \n",
    "  legend = \"radar measurements\", \n",
    "  source = radar_source)\n",
    "\n",
    "p.circle(\n",
    "  'x', 'y', size = 10, \n",
    "  fill_color = \"green\",\n",
    "  line_color = \"white\",\n",
    "  fill_alpha = 0.4,\n",
    "  line_width = 1, \n",
    "  legend = \"lidar measurements\", \n",
    "  source = lidar_source)\n",
    "\n",
    "p.line(state_xs, state_ys, line_width = 2, color='orange', legend = \"state predictions\")\n",
    "p.line(truth_xs, truth_ys, line_dash = \"4 4\", line_width = 1, color='navy', legend = \"ground truth\")\n",
    "\n",
    "p.legend.location = \"bottom_right\"\n",
    "show(p)"
   ]
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